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An Effective Task Offloading Method for Separable Complex Mobile Terminal Tasks
Author(s) -
Zemin Liu,
Na Zhou,
Yan Wang,
Jiantao Zhou,
Haotian Zhang,
Gang Xu
Publication year - 2022
Publication title -
wireless communications and mobile computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.42
H-Index - 64
eISSN - 1530-8677
pISSN - 1530-8669
DOI - 10.1155/2022/3700135
Subject(s) - computer science , mobile edge computing , cluster analysis , task (project management) , slicing , distributed computing , enhanced data rates for gsm evolution , server , terminal (telecommunication) , edge computing , process (computing) , sequence (biology) , mobile device , path (computing) , computer network , artificial intelligence , operating system , management , biology , world wide web , economics , genetics
Due to limited energy and computing power of IoT devices, they cannot handle complex tasks. Edge computing technology effectively solves the requirements of computing power and response delay for complex tasks in devices by migrating computing power to the vicinity of IoT devices. For a separable complex task on IoT terminal, we focus on the effects of data distribution, dependencies, and offloading sequence of subtasks on its total delay when it is offloaded to edge servers. Through comprehensively considering these factors, we study the slicing and choreographing method during the offloading process of a complex task. Firstly, a task slicing method based on hierarchical clustering is presented and an improved hierarchical clustering algorithm is used to obtain the optimal solution of task partitioning. Secondly, a task choreographing method based on overlapping the longest path is presented. Finally, through the simulation experiments of complex tasks with different structures and loads, the effectiveness of our method is verified.

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